In recent years, Business Intelligence (BI) technology has provided the enterprise with comprehensive business data related service, such as performing data analysis, implementing data mining, creating data reports, revealing data laws, etc. By analyzing the data and deriving a report, it may help an enterprise to make more efficient business decisions. In Business Intelligence technology, dimensionalization and hierarchization of data is the basis for subsequent data analysis utilizing a cube model.
FIG. 1 shows an example of a cube model of multi-dimension and multi-hierarchy data. In this example, data related to product sale are organized into three dimensions along three axes, namely, time (x axis), location (y axis) and product (z axis), so as to depict function relationships between sales amount and time, location and product. Further, data of sales amount are structured into a plurality of hierarchies along each dimension, and thus, data analysis and management can be performed according to hierarchies. For example, on the dimension of location, the data of sales amount are structured into sales amount at each continent; for each continent, they may be further divided into sales amount at each country; for each country, they may in turn be divided into province, city and so on as needed. Similarly, for the dimension of time, they may be structured into year, quarter, month, day and so on as needed; for dimension of product, they may be further divided according to classification, series, model of product, etc. Based on these dimensionalized and hierarchized data, OLAP (On Line Analysis Process) analysis and operation may be performed on the data by using a cube model, so as to present integrated information from each dimension and hierarchy based on user needs.
It can be seen from the above example that dimensionalization and hierarchicalization of data have provided significant convenience for data modeling and analysis in business intelligence. In addition to typical hierarchized enterprise data, it is further desired to apply analysis and operation method in business intelligence on other data. However, in many fields, such as in clinical field, data are still organized and stored in “planar” manner. FIG. 2 shows an example of existing clinical data. In the example of FIG. 2, an electronic medical record, as a typical example of clinical data, includes various kinds of data such as main symptom of a patient, diagnostic conclusion, treatment, and etc. It can be seen that, all these data are listed in a planar form with fine granularity by using clinical terminology, without showing the association between data and hierarchical information of data, which are just the basis for cube modeling and OLAP operation in intelligent analysis. Many similar planar data also exists in other business data. Due to lack of hierarchical information, it is hard to perform further analysis and management on such data by using existing intelligent modeling and operation method, which brings limitation on systematization and intelligentization of data. Therefore, it is desired to perform processing on existing planar data to obtain its hierarchical information, so as to facilitate subsequent analysis and management on the planar data.